Keywords: rail, wear, MBD Simulation, rail profiles, track measurement, wheel–rail contact.

Accurate prediction of rail profile evolution is essential for optimising maintenance planning and extending infrastructure life. Existing wear models are typically calibrated under specific conditions, limiting their reliability when extrapolated to network scale with different operational scenarios and local conditions. This paper presents a hybrid measurement-simulation framework for distributed rail wear prediction along an operational railway network. Measured rail profiles are used to derive spatially resolved effective wear coefficients across different contact regions (rail head, transition zone, and gauge face), enabling the representation of local wear behaviour along the rail profile with high reliability. Based on these measurements, characteristic wear states are defined through geometric parameters associated with specific worn areas in the contact regions. These wear states provide a compact parametric description of rail profile evolution and allow the reconstruction of profiles according to wear levels in a consistent and efficient manner. Multi-body dynamics (MBD) simulation results enable the prediction of worn rail profiles also for scenarios where measured profiles are unavailable or highly limited. The methodology is applied to a measurement campaign including multiple rail materials and curve radii, showing a good overall agreement. The proposed framework enables systematic comparison between measured and simulated profiles and provides a scalable, data-informed tool for spatially adaptive rail wear modelling and network-level maintenance planning.

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